{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":5,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# WRINKLE DETECTION","metadata":{}},{"cell_type":"markdown","source":"IMPORTING NECESSARY MODULES","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\nimport cv2\nfrom random import *\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom sklearn.model_selection import train_test_split\n\nfrom tensorflow.keras.layers import Dropout\nfrom tensorflow.keras.layers import Flatten\nfrom tensorflow.keras.layers import BatchNormalization\nfrom tensorflow.keras.layers import Dense, MaxPooling2D,Conv2D\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Input,Activation,Add\nfrom tensorflow.keras.regularizers import l2\nfrom tensorflow.keras.optimizers import Adam,Adagrad,Adadelta,Adamax,RMSprop\nfrom tensorflow.keras.callbacks import ModelCheckpoint\n","metadata":{"execution":{"iopub.status.busy":"2023-08-21T14:49:36.604049Z","iopub.execute_input":"2023-08-21T14:49:36.605244Z","iopub.status.idle":"2023-08-21T14:49:36.612871Z","shell.execute_reply.started":"2023-08-21T14:49:36.605205Z","shell.execute_reply":"2023-08-21T14:49:36.611745Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"EXTRACT DATA FROM DATASET ","metadata":{}},{"cell_type":"code","source":"fldr=\"/kaggle/input/utkface-new/UTKFace\"","metadata":{"execution":{"iopub.status.busy":"2023-08-21T14:49:36.614843Z","iopub.execute_input":"2023-08-21T14:49:36.615565Z","iopub.status.idle":"2023-08-21T14:49:36.623937Z","shell.execute_reply.started":"2023-08-21T14:49:36.615529Z","shell.execute_reply":"2023-08-21T14:49:36.622642Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"import os\nfiles=os.listdir(fldr)","metadata":{"execution":{"iopub.status.busy":"2023-08-21T14:49:36.625369Z","iopub.execute_input":"2023-08-21T14:49:36.626049Z","iopub.status.idle":"2023-08-21T14:49:37.898915Z","shell.execute_reply.started":"2023-08-21T14:49:36.626017Z","shell.execute_reply":"2023-08-21T14:49:37.897710Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":"DEFINE A BINARY LABEL THRESHOLD (AGE) FOR WRINKLE DETECTION","metadata":{}},{"cell_type":"code","source":"wrinkle_threshold = 40","metadata":{"execution":{"iopub.status.busy":"2023-08-21T14:49:37.903660Z","iopub.execute_input":"2023-08-21T14:49:37.904194Z","iopub.status.idle":"2023-08-21T14:49:37.911162Z","shell.execute_reply.started":"2023-08-21T14:49:37.904163Z","shell.execute_reply":"2023-08-21T14:49:37.910044Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"STORING IMAGES AND LABELS","metadata":{}},{"cell_type":"code","source":"isWrinkle=[]\n# genders=[]\nimages=[]\nctr = 1\nfor fle in files:\n    age=int(fle.split('_')[0])\n    \n    if age > wrinkle_threshold:\n        isWrinkle.append(1)                  # 1 - Wrinkled\n    else:\n        isWrinkle.append(0)                  # 0 - No Wrinkles\n    \n    #gender=int(fle.split('_')[1])\n    #ages.append(age)\n    #genders.append(gender)\n    total=fldr+'/'+fle\n    ctr += 1\n    image=cv2.imread(total)\n\n    image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)\n    image=cv2.resize(image,(48,48))\n    images.append(image)","metadata":{"execution":{"iopub.status.busy":"2023-08-21T14:49:37.912850Z","iopub.execute_input":"2023-08-21T14:49:37.913283Z","iopub.status.idle":"2023-08-21T14:52:30.167067Z","shell.execute_reply.started":"2023-08-21T14:49:37.913203Z","shell.execute_reply":"2023-08-21T14:52:30.165905Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":"SAVING IMAGES, ISWRINKLE AS ARRAY","metadata":{}},{"cell_type":"code","source":"images_f=np.array(images)\nisWrinkle_f=np.array(isWrinkle)","metadata":{"execution":{"iopub.status.busy":"2023-08-21T14:52:30.168601Z","iopub.execute_input":"2023-08-21T14:52:30.169001Z","iopub.status.idle":"2023-08-21T14:52:30.246085Z","shell.execute_reply.started":"2023-08-21T14:52:30.168967Z","shell.execute_reply":"2023-08-21T14:52:30.245040Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":"CREATING ANOTHER LIST OF IMAGES FROM EXISTING ONE FOR BETTER LEARNING","metadata":{}},{"cell_type":"code","source":"images_f_2=images_f/255\nimages_f_2.shape","metadata":{"execution":{"iopub.status.busy":"2023-08-21T14:52:30.247633Z","iopub.execute_input":"2023-08-21T14:52:30.248451Z","iopub.status.idle":"2023-08-21T14:52:30.674000Z","shell.execute_reply.started":"2023-08-21T14:52:30.248417Z","shell.execute_reply":"2023-08-21T14:52:30.672826Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"(23708, 48, 48, 3)"},"metadata":{}}]},{"cell_type":"markdown","source":"SPLITTING THE DATASET INTO TEST AND TRAIN","metadata":{}},{"cell_type":"code","source":"x_train,x_test,y_train,y_test = train_test_split(images_f_2,isWrinkle_f,test_size=0.25, shuffle=False)","metadata":{"execution":{"iopub.status.busy":"2023-08-21T14:52:30.675581Z","iopub.execute_input":"2023-08-21T14:52:30.676013Z","iopub.status.idle":"2023-08-21T14:52:31.068809Z","shell.execute_reply.started":"2023-08-21T14:52:30.675978Z","shell.execute_reply":"2023-08-21T14:52:31.067724Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":"DEFINING THE MODEL","metadata":{}},{"cell_type":"code","source":"def Convolution(input_tensor,filters):\n    x=Conv2D(filters=filters,kernel_size=(3,3),padding=\"same\",strides=(1,1),kernel_regularizer=l2(0.001))(input_tensor)\n    x = BatchNormalization()(x)\n    x=Activation('relu')(x)\n    x=Dropout(0.1)(x)\n    \n    return x","metadata":{"execution":{"iopub.status.busy":"2023-08-21T14:52:31.070144Z","iopub.execute_input":"2023-08-21T14:52:31.070545Z","iopub.status.idle":"2023-08-21T14:52:31.077376Z","shell.execute_reply.started":"2023-08-21T14:52:31.070513Z","shell.execute_reply":"2023-08-21T14:52:31.076316Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"def model(input_shape):\n    inputs=Input((input_shape))\n    conv_1=Convolution(inputs,32)\n    maxp_1=MaxPooling2D(pool_size=(2,2))(conv_1)\n    conv_2=Convolution(maxp_1,64)\n    maxp_2=MaxPooling2D(pool_size=(2,2))(conv_2)\n    conv_3=Convolution(maxp_2,128)\n    maxp_3=MaxPooling2D(pool_size=(2,2))(conv_3)\n    conv_4=Convolution(maxp_3,256)\n    maxp_4=MaxPooling2D(pool_size=(2,2))(conv_4)\n    flatten= Flatten()(maxp_4)\n    dense_1=Dense(64,activation='relu')(flatten)\n    dense_2=Dense(64,activation='relu')(flatten)\n    drop_1=Dropout(0.2)(dense_1)\n    drop_2=Dropout(0.2)(dense_2)\n    output_1=Dense(1,activation='sigmoid',name='sex_out')(drop_1)\n    output_2=Dense(1,activation='relu',name='age_out')(drop_2)\n    model=Model(inputs=[inputs],outputs=[output_1,output_2])\n    model.compile(loss=[\"binary_crossentropy\",\"mae\"],optimizer=\"Adam\",metrics=[\"accuracy\"])\n    return model","metadata":{"execution":{"iopub.status.busy":"2023-08-21T14:52:31.081765Z","iopub.execute_input":"2023-08-21T14:52:31.082439Z","iopub.status.idle":"2023-08-21T14:52:31.094222Z","shell.execute_reply.started":"2023-08-21T14:52:31.082405Z","shell.execute_reply":"2023-08-21T14:52:31.093142Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"model=model((48,48,3))","metadata":{"execution":{"iopub.status.busy":"2023-08-21T14:52:31.097557Z","iopub.execute_input":"2023-08-21T14:52:31.097894Z","iopub.status.idle":"2023-08-21T14:52:35.911513Z","shell.execute_reply.started":"2023-08-21T14:52:31.097860Z","shell.execute_reply":"2023-08-21T14:52:35.910479Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"execution":{"iopub.status.busy":"2023-08-21T14:52:35.912832Z","iopub.execute_input":"2023-08-21T14:52:35.913177Z","iopub.status.idle":"2023-08-21T14:52:35.965470Z","shell.execute_reply.started":"2023-08-21T14:52:35.913145Z","shell.execute_reply":"2023-08-21T14:52:35.964698Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"Model: \"model\"\n__________________________________________________________________________________________________\n Layer (type)                   Output Shape         Param #     Connected to                     \n==================================================================================================\n input_1 (InputLayer)           [(None, 48, 48, 3)]  0           []                               \n                                                                                                  \n conv2d (Conv2D)                (None, 48, 48, 32)   896         ['input_1[0][0]']                \n                                                                                                  \n batch_normalization (BatchNorm  (None, 48, 48, 32)  128         ['conv2d[0][0]']                 \n alization)                                                                                       \n                                                                                                  \n activation (Activation)        (None, 48, 48, 32)   0           ['batch_normalization[0][0]']    \n                                                                                                  \n dropout (Dropout)              (None, 48, 48, 32)   0           ['activation[0][0]']             \n                                                                                                  \n max_pooling2d (MaxPooling2D)   (None, 24, 24, 32)   0           ['dropout[0][0]']                \n                                                                                                  \n conv2d_1 (Conv2D)              (None, 24, 24, 64)   18496       ['max_pooling2d[0][0]']          \n                                                                                                  \n batch_normalization_1 (BatchNo  (None, 24, 24, 64)  256         ['conv2d_1[0][0]']               \n rmalization)                                                                                     \n                                                                                                  \n activation_1 (Activation)      (None, 24, 24, 64)   0           ['batch_normalization_1[0][0]']  \n                                                                                                  \n dropout_1 (Dropout)            (None, 24, 24, 64)   0           ['activation_1[0][0]']           \n                                                                                                  \n max_pooling2d_1 (MaxPooling2D)  (None, 12, 12, 64)  0           ['dropout_1[0][0]']              \n                                                                                                  \n conv2d_2 (Conv2D)              (None, 12, 12, 128)  73856       ['max_pooling2d_1[0][0]']        \n                                                                                                  \n batch_normalization_2 (BatchNo  (None, 12, 12, 128)  512        ['conv2d_2[0][0]']               \n rmalization)                                                                                     \n                                                                                                  \n activation_2 (Activation)      (None, 12, 12, 128)  0           ['batch_normalization_2[0][0]']  \n                                                                                                  \n dropout_2 (Dropout)            (None, 12, 12, 128)  0           ['activation_2[0][0]']           \n                                                                                                  \n max_pooling2d_2 (MaxPooling2D)  (None, 6, 6, 128)   0           ['dropout_2[0][0]']              \n                                                                                                  \n conv2d_3 (Conv2D)              (None, 6, 6, 256)    295168      ['max_pooling2d_2[0][0]']        \n                                                                                                  \n batch_normalization_3 (BatchNo  (None, 6, 6, 256)   1024        ['conv2d_3[0][0]']               \n rmalization)                                                                                     \n                                                                                                  \n activation_3 (Activation)      (None, 6, 6, 256)    0           ['batch_normalization_3[0][0]']  \n                                                                                                  \n dropout_3 (Dropout)            (None, 6, 6, 256)    0           ['activation_3[0][0]']           \n                                                                                                  \n max_pooling2d_3 (MaxPooling2D)  (None, 3, 3, 256)   0           ['dropout_3[0][0]']              \n                                                                                                  \n flatten (Flatten)              (None, 2304)         0           ['max_pooling2d_3[0][0]']        \n                                                                                                  \n dense (Dense)                  (None, 64)           147520      ['flatten[0][0]']                \n                                                                                                  \n dense_1 (Dense)                (None, 64)           147520      ['flatten[0][0]']                \n                                                                                                  \n dropout_4 (Dropout)            (None, 64)           0           ['dense[0][0]']                  \n                                                                                                  \n dropout_5 (Dropout)            (None, 64)           0           ['dense_1[0][0]']                \n                                                                                                  \n sex_out (Dense)                (None, 1)            65          ['dropout_4[0][0]']              \n                                                                                                  \n age_out (Dense)                (None, 1)            65          ['dropout_5[0][0]']              \n                                                                                                  \n==================================================================================================\nTotal params: 685,506\nTrainable params: 684,546\nNon-trainable params: 960\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"markdown","source":"INITIALIZING THE MODEL","metadata":{}},{"cell_type":"code","source":"fle_s='Wrinkle_Detection.h5'\ncheckpoint=ModelCheckpoint(fle_s,monitor='val_loss',verbose=1,save_best_only=True,save_weights_only=False,mode='auto',save_freq='epoch')\nEarly_stop=tf.keras.callbacks.EarlyStopping(patience=75,monitor='val_loss',restore_best_weights='True')\ncallback_list=[checkpoint,Early_stop]","metadata":{"execution":{"iopub.status.busy":"2023-08-21T14:52:35.966441Z","iopub.execute_input":"2023-08-21T14:52:35.966855Z","iopub.status.idle":"2023-08-21T14:52:35.980660Z","shell.execute_reply.started":"2023-08-21T14:52:35.966807Z","shell.execute_reply":"2023-08-21T14:52:35.979884Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":"RUNNING THE MODEL","metadata":{}},{"cell_type":"code","source":"history=model.fit(\n    x = x_train,\n    y = y_train,\n    batch_size=64,\n    validation_data=(x_test,y_test),\n    epochs=100,\n    callbacks=callback_list\n)","metadata":{"execution":{"iopub.status.busy":"2023-08-21T14:52:35.981792Z","iopub.execute_input":"2023-08-21T14:52:35.982195Z","iopub.status.idle":"2023-08-21T14:59:38.976282Z","shell.execute_reply.started":"2023-08-21T14:52:35.982161Z","shell.execute_reply":"2023-08-21T14:59:38.975256Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"Epoch 1/100\n","output_type":"stream"},{"name":"stderr","text":"2023-08-21 14:52:39.282401: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inmodel/dropout/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\n","output_type":"stream"},{"name":"stdout","text":"278/278 [==============================] - ETA: 0s - loss: 1.0280 - sex_out_loss: 0.4776 - age_out_loss: 0.2831 - sex_out_accuracy: 0.7728 - age_out_accuracy: 0.7169\nEpoch 1: val_loss improved from inf to 1.03608, saving model to Wrinkle_Detection.h5\n278/278 [==============================] - 20s 22ms/step - loss: 1.0280 - sex_out_loss: 0.4776 - age_out_loss: 0.2831 - sex_out_accuracy: 0.7728 - age_out_accuracy: 0.7169 - val_loss: 1.0361 - val_sex_out_loss: 0.5403 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.7613 - val_age_out_accuracy: 0.7155\nEpoch 2/100\n276/278 [============================>.] - ETA: 0s - loss: 0.8189 - sex_out_loss: 0.3653 - age_out_loss: 0.2822 - sex_out_accuracy: 0.8384 - age_out_accuracy: 0.7178\nEpoch 2: val_loss did not improve from 1.03608\n278/278 [==============================] - 5s 17ms/step - loss: 0.8189 - sex_out_loss: 0.3654 - age_out_loss: 0.2824 - sex_out_accuracy: 0.8381 - age_out_accuracy: 0.7176 - val_loss: 1.0498 - val_sex_out_loss: 0.6264 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.6477 - val_age_out_accuracy: 0.7155\nEpoch 3/100\n275/278 [============================>.] - ETA: 0s - loss: 0.7375 - sex_out_loss: 0.3315 - age_out_loss: 0.2829 - sex_out_accuracy: 0.8576 - age_out_accuracy: 0.7171\nEpoch 3: val_loss improved from 1.03608 to 0.71393, saving model to Wrinkle_Detection.h5\n278/278 [==============================] - 5s 19ms/step - loss: 0.7363 - sex_out_loss: 0.3310 - age_out_loss: 0.2824 - sex_out_accuracy: 0.8578 - age_out_accuracy: 0.7176 - val_loss: 0.7139 - val_sex_out_loss: 0.3196 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8743 - val_age_out_accuracy: 0.7155\nEpoch 4/100\n275/278 [============================>.] - ETA: 0s - loss: 0.6881 - sex_out_loss: 0.3035 - age_out_loss: 0.2818 - sex_out_accuracy: 0.8728 - age_out_accuracy: 0.7182\nEpoch 4: val_loss did not improve from 0.71393\n278/278 [==============================] - 5s 18ms/step - loss: 0.6890 - sex_out_loss: 0.3038 - age_out_loss: 0.2824 - sex_out_accuracy: 0.8727 - age_out_accuracy: 0.7176 - val_loss: 1.1804 - val_sex_out_loss: 0.7972 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.6037 - val_age_out_accuracy: 0.7155\nEpoch 5/100\n278/278 [==============================] - ETA: 0s - loss: 0.6717 - sex_out_loss: 0.2917 - age_out_loss: 0.2824 - sex_out_accuracy: 0.8790 - age_out_accuracy: 0.7176\nEpoch 5: val_loss improved from 0.71393 to 0.69830, saving model to Wrinkle_Detection.h5\n278/278 [==============================] - 5s 18ms/step - loss: 0.6717 - sex_out_loss: 0.2917 - age_out_loss: 0.2824 - sex_out_accuracy: 0.8790 - age_out_accuracy: 0.7176 - val_loss: 0.6983 - val_sex_out_loss: 0.3177 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8743 - val_age_out_accuracy: 0.7155\nEpoch 6/100\n278/278 [==============================] - ETA: 0s - loss: 0.6599 - sex_out_loss: 0.2816 - age_out_loss: 0.2824 - sex_out_accuracy: 0.8846 - age_out_accuracy: 0.7176\nEpoch 6: val_loss did not improve from 0.69830\n278/278 [==============================] - 5s 17ms/step - loss: 0.6599 - sex_out_loss: 0.2816 - age_out_loss: 0.2824 - sex_out_accuracy: 0.8846 - age_out_accuracy: 0.7176 - val_loss: 0.7455 - val_sex_out_loss: 0.3642 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8301 - val_age_out_accuracy: 0.7155\nEpoch 7/100\n277/278 [============================>.] - ETA: 0s - loss: 0.6458 - sex_out_loss: 0.2669 - age_out_loss: 0.2824 - sex_out_accuracy: 0.8899 - age_out_accuracy: 0.7176\nEpoch 7: val_loss improved from 0.69830 to 0.66893, saving model to Wrinkle_Detection.h5\n278/278 [==============================] - 5s 18ms/step - loss: 0.6461 - sex_out_loss: 0.2672 - age_out_loss: 0.2824 - sex_out_accuracy: 0.8899 - age_out_accuracy: 0.7176 - val_loss: 0.6689 - val_sex_out_loss: 0.2898 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8709 - val_age_out_accuracy: 0.7155\nEpoch 8/100\n277/278 [============================>.] - ETA: 0s - loss: 0.6417 - sex_out_loss: 0.2614 - age_out_loss: 0.2826 - sex_out_accuracy: 0.8914 - age_out_accuracy: 0.7174\nEpoch 8: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.6414 - sex_out_loss: 0.2613 - age_out_loss: 0.2824 - sex_out_accuracy: 0.8914 - age_out_accuracy: 0.7176 - val_loss: 0.6796 - val_sex_out_loss: 0.2956 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8743 - val_age_out_accuracy: 0.7155\nEpoch 9/100\n278/278 [==============================] - ETA: 0s - loss: 0.6426 - sex_out_loss: 0.2592 - age_out_loss: 0.2824 - sex_out_accuracy: 0.8935 - age_out_accuracy: 0.7176\nEpoch 9: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.6426 - sex_out_loss: 0.2592 - age_out_loss: 0.2824 - sex_out_accuracy: 0.8935 - age_out_accuracy: 0.7176 - val_loss: 0.8991 - val_sex_out_loss: 0.5137 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.7631 - val_age_out_accuracy: 0.7155\nEpoch 10/100\n276/278 [============================>.] - ETA: 0s - loss: 0.6368 - sex_out_loss: 0.2498 - age_out_loss: 0.2824 - sex_out_accuracy: 0.8987 - age_out_accuracy: 0.7176\nEpoch 10: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.6368 - sex_out_loss: 0.2498 - age_out_loss: 0.2824 - sex_out_accuracy: 0.8988 - age_out_accuracy: 0.7176 - val_loss: 0.9344 - val_sex_out_loss: 0.5431 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.7279 - val_age_out_accuracy: 0.7155\nEpoch 11/100\n278/278 [==============================] - ETA: 0s - loss: 0.6333 - sex_out_loss: 0.2434 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9016 - age_out_accuracy: 0.7176\nEpoch 11: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.6333 - sex_out_loss: 0.2434 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9016 - age_out_accuracy: 0.7176 - val_loss: 0.6996 - val_sex_out_loss: 0.3072 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8701 - val_age_out_accuracy: 0.7155\nEpoch 12/100\n276/278 [============================>.] - ETA: 0s - loss: 0.6292 - sex_out_loss: 0.2383 - age_out_loss: 0.2823 - sex_out_accuracy: 0.9048 - age_out_accuracy: 0.7177\nEpoch 12: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.6293 - sex_out_loss: 0.2382 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9048 - age_out_accuracy: 0.7176 - val_loss: 0.8100 - val_sex_out_loss: 0.4142 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8007 - val_age_out_accuracy: 0.7155\nEpoch 13/100\n275/278 [============================>.] - ETA: 0s - loss: 0.6252 - sex_out_loss: 0.2292 - age_out_loss: 0.2823 - sex_out_accuracy: 0.9060 - age_out_accuracy: 0.7177\nEpoch 13: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.6259 - sex_out_loss: 0.2296 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9057 - age_out_accuracy: 0.7176 - val_loss: 0.8615 - val_sex_out_loss: 0.4607 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.7660 - val_age_out_accuracy: 0.7155\nEpoch 14/100\n278/278 [==============================] - ETA: 0s - loss: 0.6261 - sex_out_loss: 0.2283 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9078 - age_out_accuracy: 0.7176\nEpoch 14: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.6261 - sex_out_loss: 0.2283 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9078 - age_out_accuracy: 0.7176 - val_loss: 0.6822 - val_sex_out_loss: 0.2811 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8851 - val_age_out_accuracy: 0.7155\nEpoch 15/100\n278/278 [==============================] - ETA: 0s - loss: 0.6191 - sex_out_loss: 0.2199 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9123 - age_out_accuracy: 0.7176\nEpoch 15: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.6191 - sex_out_loss: 0.2199 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9123 - age_out_accuracy: 0.7176 - val_loss: 0.6883 - val_sex_out_loss: 0.2859 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8812 - val_age_out_accuracy: 0.7155\nEpoch 16/100\n276/278 [============================>.] - ETA: 0s - loss: 0.6154 - sex_out_loss: 0.2138 - age_out_loss: 0.2820 - sex_out_accuracy: 0.9138 - age_out_accuracy: 0.7180\nEpoch 16: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 19ms/step - loss: 0.6166 - sex_out_loss: 0.2146 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9137 - age_out_accuracy: 0.7176 - val_loss: 0.7596 - val_sex_out_loss: 0.3542 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8463 - val_age_out_accuracy: 0.7155\nEpoch 17/100\n277/278 [============================>.] - ETA: 0s - loss: 0.6187 - sex_out_loss: 0.2141 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9143 - age_out_accuracy: 0.7176\nEpoch 17: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.6188 - sex_out_loss: 0.2142 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9144 - age_out_accuracy: 0.7176 - val_loss: 0.7454 - val_sex_out_loss: 0.3396 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8554 - val_age_out_accuracy: 0.7155\nEpoch 18/100\n275/278 [============================>.] - ETA: 0s - loss: 0.6146 - sex_out_loss: 0.2074 - age_out_loss: 0.2822 - sex_out_accuracy: 0.9182 - age_out_accuracy: 0.7178\nEpoch 18: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.6147 - sex_out_loss: 0.2073 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9183 - age_out_accuracy: 0.7176 - val_loss: 0.6846 - val_sex_out_loss: 0.2756 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8856 - val_age_out_accuracy: 0.7155\nEpoch 19/100\n278/278 [==============================] - ETA: 0s - loss: 0.6084 - sex_out_loss: 0.2007 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9181 - age_out_accuracy: 0.7176\nEpoch 19: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.6084 - sex_out_loss: 0.2007 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9181 - age_out_accuracy: 0.7176 - val_loss: 0.7016 - val_sex_out_loss: 0.2932 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8757 - val_age_out_accuracy: 0.7155\nEpoch 20/100\n275/278 [============================>.] - ETA: 0s - loss: 0.6040 - sex_out_loss: 0.1948 - age_out_loss: 0.2829 - sex_out_accuracy: 0.9237 - age_out_accuracy: 0.7171\nEpoch 20: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.6038 - sex_out_loss: 0.1951 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9237 - age_out_accuracy: 0.7176 - val_loss: 0.6834 - val_sex_out_loss: 0.2722 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8878 - val_age_out_accuracy: 0.7155\nEpoch 21/100\n277/278 [============================>.] - ETA: 0s - loss: 0.6045 - sex_out_loss: 0.1928 - age_out_loss: 0.2827 - sex_out_accuracy: 0.9227 - age_out_accuracy: 0.7173\nEpoch 21: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.6040 - sex_out_loss: 0.1925 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9228 - age_out_accuracy: 0.7176 - val_loss: 0.7614 - val_sex_out_loss: 0.3464 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8448 - val_age_out_accuracy: 0.7155\nEpoch 22/100\n278/278 [==============================] - ETA: 0s - loss: 0.6047 - sex_out_loss: 0.1894 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9275 - age_out_accuracy: 0.7176\nEpoch 22: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 19ms/step - loss: 0.6047 - sex_out_loss: 0.1894 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9275 - age_out_accuracy: 0.7176 - val_loss: 0.6819 - val_sex_out_loss: 0.2668 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8870 - val_age_out_accuracy: 0.7155\nEpoch 23/100\n275/278 [============================>.] - ETA: 0s - loss: 0.5958 - sex_out_loss: 0.1819 - age_out_loss: 0.2826 - sex_out_accuracy: 0.9285 - age_out_accuracy: 0.7174\nEpoch 23: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5959 - sex_out_loss: 0.1821 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9282 - age_out_accuracy: 0.7176 - val_loss: 0.7177 - val_sex_out_loss: 0.3017 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8762 - val_age_out_accuracy: 0.7155\nEpoch 24/100\n278/278 [==============================] - ETA: 0s - loss: 0.6008 - sex_out_loss: 0.1839 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9264 - age_out_accuracy: 0.7176\nEpoch 24: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.6008 - sex_out_loss: 0.1839 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9264 - age_out_accuracy: 0.7176 - val_loss: 0.7219 - val_sex_out_loss: 0.3026 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8699 - val_age_out_accuracy: 0.7155\nEpoch 25/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5889 - sex_out_loss: 0.1728 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9333 - age_out_accuracy: 0.7176\nEpoch 25: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5889 - sex_out_loss: 0.1727 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9334 - age_out_accuracy: 0.7176 - val_loss: 0.9861 - val_sex_out_loss: 0.5684 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.7545 - val_age_out_accuracy: 0.7155\nEpoch 26/100\n278/278 [==============================] - ETA: 0s - loss: 0.5880 - sex_out_loss: 0.1715 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9313 - age_out_accuracy: 0.7176\nEpoch 26: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5880 - sex_out_loss: 0.1715 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9313 - age_out_accuracy: 0.7176 - val_loss: 0.7111 - val_sex_out_loss: 0.2909 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8795 - val_age_out_accuracy: 0.7155\nEpoch 27/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5850 - sex_out_loss: 0.1658 - age_out_loss: 0.2826 - sex_out_accuracy: 0.9363 - age_out_accuracy: 0.7174\nEpoch 27: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5852 - sex_out_loss: 0.1661 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9363 - age_out_accuracy: 0.7176 - val_loss: 0.7161 - val_sex_out_loss: 0.2936 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8689 - val_age_out_accuracy: 0.7155\nEpoch 28/100\n278/278 [==============================] - ETA: 0s - loss: 0.5835 - sex_out_loss: 0.1635 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9378 - age_out_accuracy: 0.7176\nEpoch 28: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5835 - sex_out_loss: 0.1635 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9378 - age_out_accuracy: 0.7176 - val_loss: 0.7208 - val_sex_out_loss: 0.2981 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8758 - val_age_out_accuracy: 0.7155\nEpoch 29/100\n275/278 [============================>.] - ETA: 0s - loss: 0.5880 - sex_out_loss: 0.1680 - age_out_loss: 0.2826 - sex_out_accuracy: 0.9351 - age_out_accuracy: 0.7174\nEpoch 29: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5878 - sex_out_loss: 0.1679 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9350 - age_out_accuracy: 0.7176 - val_loss: 0.7103 - val_sex_out_loss: 0.2880 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8768 - val_age_out_accuracy: 0.7155\nEpoch 30/100\n278/278 [==============================] - ETA: 0s - loss: 0.5709 - sex_out_loss: 0.1516 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9413 - age_out_accuracy: 0.7176\nEpoch 30: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5709 - sex_out_loss: 0.1516 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9413 - age_out_accuracy: 0.7176 - val_loss: 0.7039 - val_sex_out_loss: 0.2818 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8826 - val_age_out_accuracy: 0.7155\nEpoch 31/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5772 - sex_out_loss: 0.1562 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9391 - age_out_accuracy: 0.7176\nEpoch 31: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5771 - sex_out_loss: 0.1561 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9390 - age_out_accuracy: 0.7176 - val_loss: 0.7569 - val_sex_out_loss: 0.3339 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8453 - val_age_out_accuracy: 0.7155\nEpoch 32/100\n278/278 [==============================] - ETA: 0s - loss: 0.5807 - sex_out_loss: 0.1563 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9396 - age_out_accuracy: 0.7176\nEpoch 32: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5807 - sex_out_loss: 0.1563 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9396 - age_out_accuracy: 0.7176 - val_loss: 0.7274 - val_sex_out_loss: 0.2995 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8851 - val_age_out_accuracy: 0.7155\nEpoch 33/100\n278/278 [==============================] - ETA: 0s - loss: 0.5727 - sex_out_loss: 0.1474 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9437 - age_out_accuracy: 0.7176\nEpoch 33: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5727 - sex_out_loss: 0.1474 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9437 - age_out_accuracy: 0.7176 - val_loss: 0.7705 - val_sex_out_loss: 0.3421 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8755 - val_age_out_accuracy: 0.7155\nEpoch 34/100\n276/278 [============================>.] - ETA: 0s - loss: 0.5683 - sex_out_loss: 0.1433 - age_out_loss: 0.2827 - sex_out_accuracy: 0.9425 - age_out_accuracy: 0.7173\nEpoch 34: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5684 - sex_out_loss: 0.1437 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9425 - age_out_accuracy: 0.7176 - val_loss: 0.7911 - val_sex_out_loss: 0.3649 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8515 - val_age_out_accuracy: 0.7155\nEpoch 35/100\n278/278 [==============================] - ETA: 0s - loss: 0.5676 - sex_out_loss: 0.1424 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9439 - age_out_accuracy: 0.7176\nEpoch 35: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5676 - sex_out_loss: 0.1424 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9439 - age_out_accuracy: 0.7176 - val_loss: 0.7181 - val_sex_out_loss: 0.2895 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8782 - val_age_out_accuracy: 0.7155\nEpoch 36/100\n275/278 [============================>.] - ETA: 0s - loss: 0.5651 - sex_out_loss: 0.1400 - age_out_loss: 0.2822 - sex_out_accuracy: 0.9456 - age_out_accuracy: 0.7178\nEpoch 36: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5658 - sex_out_loss: 0.1404 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9454 - age_out_accuracy: 0.7176 - val_loss: 0.7951 - val_sex_out_loss: 0.3669 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8586 - val_age_out_accuracy: 0.7155\nEpoch 37/100\n278/278 [==============================] - ETA: 0s - loss: 0.5656 - sex_out_loss: 0.1398 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9454 - age_out_accuracy: 0.7176\nEpoch 37: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5656 - sex_out_loss: 0.1398 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9454 - age_out_accuracy: 0.7176 - val_loss: 0.7491 - val_sex_out_loss: 0.3206 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8863 - val_age_out_accuracy: 0.7155\nEpoch 38/100\n278/278 [==============================] - ETA: 0s - loss: 0.5559 - sex_out_loss: 0.1302 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9497 - age_out_accuracy: 0.7176\nEpoch 38: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5559 - sex_out_loss: 0.1302 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9497 - age_out_accuracy: 0.7176 - val_loss: 0.7368 - val_sex_out_loss: 0.3090 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8851 - val_age_out_accuracy: 0.7155\nEpoch 39/100\n275/278 [============================>.] - ETA: 0s - loss: 0.5551 - sex_out_loss: 0.1292 - age_out_loss: 0.2822 - sex_out_accuracy: 0.9504 - age_out_accuracy: 0.7178\nEpoch 39: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5559 - sex_out_loss: 0.1298 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9501 - age_out_accuracy: 0.7176 - val_loss: 0.8228 - val_sex_out_loss: 0.3931 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8178 - val_age_out_accuracy: 0.7155\nEpoch 40/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5556 - sex_out_loss: 0.1299 - age_out_loss: 0.2821 - sex_out_accuracy: 0.9515 - age_out_accuracy: 0.7179\nEpoch 40: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5565 - sex_out_loss: 0.1304 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9513 - age_out_accuracy: 0.7176 - val_loss: 0.7423 - val_sex_out_loss: 0.3138 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8758 - val_age_out_accuracy: 0.7155\nEpoch 41/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5530 - sex_out_loss: 0.1264 - age_out_loss: 0.2825 - sex_out_accuracy: 0.9493 - age_out_accuracy: 0.7175\nEpoch 41: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5528 - sex_out_loss: 0.1263 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9493 - age_out_accuracy: 0.7176 - val_loss: 0.9288 - val_sex_out_loss: 0.5010 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8107 - val_age_out_accuracy: 0.7155\nEpoch 42/100\n278/278 [==============================] - ETA: 0s - loss: 0.5571 - sex_out_loss: 0.1307 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9479 - age_out_accuracy: 0.7176\nEpoch 42: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5571 - sex_out_loss: 0.1307 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9479 - age_out_accuracy: 0.7176 - val_loss: 0.7277 - val_sex_out_loss: 0.2989 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8746 - val_age_out_accuracy: 0.7155\nEpoch 43/100\n278/278 [==============================] - ETA: 0s - loss: 0.5517 - sex_out_loss: 0.1255 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9520 - age_out_accuracy: 0.7176\nEpoch 43: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5517 - sex_out_loss: 0.1255 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9520 - age_out_accuracy: 0.7176 - val_loss: 0.7524 - val_sex_out_loss: 0.3233 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8714 - val_age_out_accuracy: 0.7155\nEpoch 44/100\n276/278 [============================>.] - ETA: 0s - loss: 0.5573 - sex_out_loss: 0.1299 - age_out_loss: 0.2822 - sex_out_accuracy: 0.9492 - age_out_accuracy: 0.7178\nEpoch 44: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5573 - sex_out_loss: 0.1296 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9493 - age_out_accuracy: 0.7176 - val_loss: 0.8028 - val_sex_out_loss: 0.3725 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8814 - val_age_out_accuracy: 0.7155\nEpoch 45/100\n276/278 [============================>.] - ETA: 0s - loss: 0.5546 - sex_out_loss: 0.1268 - age_out_loss: 0.2822 - sex_out_accuracy: 0.9505 - age_out_accuracy: 0.7178\nEpoch 45: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5547 - sex_out_loss: 0.1267 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9506 - age_out_accuracy: 0.7176 - val_loss: 1.0293 - val_sex_out_loss: 0.5989 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.7589 - val_age_out_accuracy: 0.7155\nEpoch 46/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5501 - sex_out_loss: 0.1227 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9523 - age_out_accuracy: 0.7176\nEpoch 46: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5504 - sex_out_loss: 0.1229 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9520 - age_out_accuracy: 0.7176 - val_loss: 0.7432 - val_sex_out_loss: 0.3144 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8684 - val_age_out_accuracy: 0.7155\nEpoch 47/100\n275/278 [============================>.] - ETA: 0s - loss: 0.5470 - sex_out_loss: 0.1197 - age_out_loss: 0.2826 - sex_out_accuracy: 0.9534 - age_out_accuracy: 0.7174\nEpoch 47: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5464 - sex_out_loss: 0.1192 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9537 - age_out_accuracy: 0.7176 - val_loss: 0.8615 - val_sex_out_loss: 0.4319 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8443 - val_age_out_accuracy: 0.7155\nEpoch 48/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5447 - sex_out_loss: 0.1180 - age_out_loss: 0.2823 - sex_out_accuracy: 0.9539 - age_out_accuracy: 0.7177\nEpoch 48: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 19ms/step - loss: 0.5448 - sex_out_loss: 0.1179 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9539 - age_out_accuracy: 0.7176 - val_loss: 0.7916 - val_sex_out_loss: 0.3631 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8763 - val_age_out_accuracy: 0.7155\nEpoch 49/100\n276/278 [============================>.] - ETA: 0s - loss: 0.5530 - sex_out_loss: 0.1263 - age_out_loss: 0.2822 - sex_out_accuracy: 0.9496 - age_out_accuracy: 0.7178\nEpoch 49: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5538 - sex_out_loss: 0.1268 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9494 - age_out_accuracy: 0.7176 - val_loss: 0.7571 - val_sex_out_loss: 0.3265 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8721 - val_age_out_accuracy: 0.7155\nEpoch 50/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5518 - sex_out_loss: 0.1230 - age_out_loss: 0.2823 - sex_out_accuracy: 0.9521 - age_out_accuracy: 0.7177\nEpoch 50: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5526 - sex_out_loss: 0.1236 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9519 - age_out_accuracy: 0.7176 - val_loss: 0.9742 - val_sex_out_loss: 0.5435 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.7883 - val_age_out_accuracy: 0.7155\nEpoch 51/100\n275/278 [============================>.] - ETA: 0s - loss: 0.5471 - sex_out_loss: 0.1194 - age_out_loss: 0.2821 - sex_out_accuracy: 0.9552 - age_out_accuracy: 0.7179\nEpoch 51: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5475 - sex_out_loss: 0.1194 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9550 - age_out_accuracy: 0.7176 - val_loss: 0.7672 - val_sex_out_loss: 0.3377 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8728 - val_age_out_accuracy: 0.7155\nEpoch 52/100\n276/278 [============================>.] - ETA: 0s - loss: 0.5416 - sex_out_loss: 0.1137 - age_out_loss: 0.2827 - sex_out_accuracy: 0.9574 - age_out_accuracy: 0.7173\nEpoch 52: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5416 - sex_out_loss: 0.1139 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9571 - age_out_accuracy: 0.7176 - val_loss: 0.8703 - val_sex_out_loss: 0.4413 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8277 - val_age_out_accuracy: 0.7155\nEpoch 53/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5459 - sex_out_loss: 0.1192 - age_out_loss: 0.2822 - sex_out_accuracy: 0.9540 - age_out_accuracy: 0.7178\nEpoch 53: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5460 - sex_out_loss: 0.1190 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9541 - age_out_accuracy: 0.7176 - val_loss: 0.7551 - val_sex_out_loss: 0.3259 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8676 - val_age_out_accuracy: 0.7155\nEpoch 54/100\n275/278 [============================>.] - ETA: 0s - loss: 0.5411 - sex_out_loss: 0.1131 - age_out_loss: 0.2833 - sex_out_accuracy: 0.9574 - age_out_accuracy: 0.7167\nEpoch 54: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 19ms/step - loss: 0.5406 - sex_out_loss: 0.1134 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9571 - age_out_accuracy: 0.7176 - val_loss: 0.7586 - val_sex_out_loss: 0.3282 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8787 - val_age_out_accuracy: 0.7155\nEpoch 55/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5417 - sex_out_loss: 0.1136 - age_out_loss: 0.2823 - sex_out_accuracy: 0.9561 - age_out_accuracy: 0.7177\nEpoch 55: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5417 - sex_out_loss: 0.1134 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9561 - age_out_accuracy: 0.7176 - val_loss: 0.9154 - val_sex_out_loss: 0.4848 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8627 - val_age_out_accuracy: 0.7155\nEpoch 56/100\n276/278 [============================>.] - ETA: 0s - loss: 0.5472 - sex_out_loss: 0.1190 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9518 - age_out_accuracy: 0.7176\nEpoch 56: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5471 - sex_out_loss: 0.1190 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9517 - age_out_accuracy: 0.7176 - val_loss: 0.7695 - val_sex_out_loss: 0.3392 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8805 - val_age_out_accuracy: 0.7155\nEpoch 57/100\n276/278 [============================>.] - ETA: 0s - loss: 0.5447 - sex_out_loss: 0.1159 - age_out_loss: 0.2828 - sex_out_accuracy: 0.9554 - age_out_accuracy: 0.7172\nEpoch 57: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5446 - sex_out_loss: 0.1162 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9552 - age_out_accuracy: 0.7176 - val_loss: 0.7781 - val_sex_out_loss: 0.3471 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8731 - val_age_out_accuracy: 0.7155\nEpoch 58/100\n278/278 [==============================] - ETA: 0s - loss: 0.5334 - sex_out_loss: 0.1059 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9591 - age_out_accuracy: 0.7176\nEpoch 58: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5334 - sex_out_loss: 0.1059 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9591 - age_out_accuracy: 0.7176 - val_loss: 0.7642 - val_sex_out_loss: 0.3343 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8711 - val_age_out_accuracy: 0.7155\nEpoch 59/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5350 - sex_out_loss: 0.1069 - age_out_loss: 0.2825 - sex_out_accuracy: 0.9584 - age_out_accuracy: 0.7175\nEpoch 59: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5352 - sex_out_loss: 0.1071 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9583 - age_out_accuracy: 0.7176 - val_loss: 0.7447 - val_sex_out_loss: 0.3139 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8865 - val_age_out_accuracy: 0.7155\nEpoch 60/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5403 - sex_out_loss: 0.1116 - age_out_loss: 0.2828 - sex_out_accuracy: 0.9578 - age_out_accuracy: 0.7172\nEpoch 60: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5399 - sex_out_loss: 0.1116 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9578 - age_out_accuracy: 0.7176 - val_loss: 0.7379 - val_sex_out_loss: 0.3085 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8843 - val_age_out_accuracy: 0.7155\nEpoch 61/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5319 - sex_out_loss: 0.1049 - age_out_loss: 0.2828 - sex_out_accuracy: 0.9591 - age_out_accuracy: 0.7172\nEpoch 61: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5313 - sex_out_loss: 0.1047 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9592 - age_out_accuracy: 0.7176 - val_loss: 0.8561 - val_sex_out_loss: 0.4273 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8289 - val_age_out_accuracy: 0.7155\nEpoch 62/100\n276/278 [============================>.] - ETA: 0s - loss: 0.5309 - sex_out_loss: 0.1048 - age_out_loss: 0.2821 - sex_out_accuracy: 0.9597 - age_out_accuracy: 0.7179\nEpoch 62: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5317 - sex_out_loss: 0.1052 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9595 - age_out_accuracy: 0.7176 - val_loss: 0.7627 - val_sex_out_loss: 0.3344 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8794 - val_age_out_accuracy: 0.7155\nEpoch 63/100\n278/278 [==============================] - ETA: 0s - loss: 0.5355 - sex_out_loss: 0.1087 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9573 - age_out_accuracy: 0.7176\nEpoch 63: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5355 - sex_out_loss: 0.1087 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9573 - age_out_accuracy: 0.7176 - val_loss: 0.7313 - val_sex_out_loss: 0.3020 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8807 - val_age_out_accuracy: 0.7155\nEpoch 64/100\n275/278 [============================>.] - ETA: 0s - loss: 0.5378 - sex_out_loss: 0.1102 - age_out_loss: 0.2828 - sex_out_accuracy: 0.9568 - age_out_accuracy: 0.7172\nEpoch 64: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5374 - sex_out_loss: 0.1101 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9569 - age_out_accuracy: 0.7176 - val_loss: 0.8200 - val_sex_out_loss: 0.3912 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8733 - val_age_out_accuracy: 0.7155\nEpoch 65/100\n275/278 [============================>.] - ETA: 0s - loss: 0.5290 - sex_out_loss: 0.1030 - age_out_loss: 0.2823 - sex_out_accuracy: 0.9589 - age_out_accuracy: 0.7177\nEpoch 65: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5291 - sex_out_loss: 0.1030 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9588 - age_out_accuracy: 0.7176 - val_loss: 0.8288 - val_sex_out_loss: 0.3988 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8530 - val_age_out_accuracy: 0.7155\nEpoch 66/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5306 - sex_out_loss: 0.1024 - age_out_loss: 0.2826 - sex_out_accuracy: 0.9615 - age_out_accuracy: 0.7174\nEpoch 66: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5303 - sex_out_loss: 0.1023 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9616 - age_out_accuracy: 0.7176 - val_loss: 0.7702 - val_sex_out_loss: 0.3409 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8677 - val_age_out_accuracy: 0.7155\nEpoch 67/100\n276/278 [============================>.] - ETA: 0s - loss: 0.5316 - sex_out_loss: 0.1052 - age_out_loss: 0.2822 - sex_out_accuracy: 0.9584 - age_out_accuracy: 0.7178\nEpoch 67: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5316 - sex_out_loss: 0.1050 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9584 - age_out_accuracy: 0.7176 - val_loss: 0.7755 - val_sex_out_loss: 0.3476 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8546 - val_age_out_accuracy: 0.7155\nEpoch 68/100\n278/278 [==============================] - ETA: 0s - loss: 0.5322 - sex_out_loss: 0.1063 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9576 - age_out_accuracy: 0.7176\nEpoch 68: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5322 - sex_out_loss: 0.1063 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9576 - age_out_accuracy: 0.7176 - val_loss: 0.8389 - val_sex_out_loss: 0.4096 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8180 - val_age_out_accuracy: 0.7155\nEpoch 69/100\n275/278 [============================>.] - ETA: 0s - loss: 0.5324 - sex_out_loss: 0.1053 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9584 - age_out_accuracy: 0.7176\nEpoch 69: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5324 - sex_out_loss: 0.1053 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9584 - age_out_accuracy: 0.7176 - val_loss: 0.7554 - val_sex_out_loss: 0.3260 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8790 - val_age_out_accuracy: 0.7155\nEpoch 70/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5256 - sex_out_loss: 0.0991 - age_out_loss: 0.2825 - sex_out_accuracy: 0.9619 - age_out_accuracy: 0.7175\nEpoch 70: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5255 - sex_out_loss: 0.0991 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9618 - age_out_accuracy: 0.7176 - val_loss: 0.7517 - val_sex_out_loss: 0.3233 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8797 - val_age_out_accuracy: 0.7155\nEpoch 71/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5276 - sex_out_loss: 0.1016 - age_out_loss: 0.2827 - sex_out_accuracy: 0.9610 - age_out_accuracy: 0.7173\nEpoch 71: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5274 - sex_out_loss: 0.1016 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9610 - age_out_accuracy: 0.7176 - val_loss: 0.8931 - val_sex_out_loss: 0.4654 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8340 - val_age_out_accuracy: 0.7155\nEpoch 72/100\n278/278 [==============================] - ETA: 0s - loss: 0.5285 - sex_out_loss: 0.1025 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9614 - age_out_accuracy: 0.7176\nEpoch 72: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5285 - sex_out_loss: 0.1025 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9614 - age_out_accuracy: 0.7176 - val_loss: 0.7593 - val_sex_out_loss: 0.3307 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8750 - val_age_out_accuracy: 0.7155\nEpoch 73/100\n276/278 [============================>.] - ETA: 0s - loss: 0.5308 - sex_out_loss: 0.1045 - age_out_loss: 0.2821 - sex_out_accuracy: 0.9601 - age_out_accuracy: 0.7179\nEpoch 73: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5312 - sex_out_loss: 0.1045 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9601 - age_out_accuracy: 0.7176 - val_loss: 0.7980 - val_sex_out_loss: 0.3689 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8724 - val_age_out_accuracy: 0.7155\nEpoch 74/100\n276/278 [============================>.] - ETA: 0s - loss: 0.5314 - sex_out_loss: 0.1045 - age_out_loss: 0.2827 - sex_out_accuracy: 0.9590 - age_out_accuracy: 0.7173\nEpoch 74: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5311 - sex_out_loss: 0.1043 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9590 - age_out_accuracy: 0.7176 - val_loss: 0.8082 - val_sex_out_loss: 0.3792 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8628 - val_age_out_accuracy: 0.7155\nEpoch 75/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5267 - sex_out_loss: 0.1006 - age_out_loss: 0.2823 - sex_out_accuracy: 0.9627 - age_out_accuracy: 0.7177\nEpoch 75: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5267 - sex_out_loss: 0.1004 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9628 - age_out_accuracy: 0.7176 - val_loss: 0.7593 - val_sex_out_loss: 0.3316 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8784 - val_age_out_accuracy: 0.7155\nEpoch 76/100\n275/278 [============================>.] - ETA: 0s - loss: 0.5207 - sex_out_loss: 0.0957 - age_out_loss: 0.2823 - sex_out_accuracy: 0.9636 - age_out_accuracy: 0.7177\nEpoch 76: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5216 - sex_out_loss: 0.0965 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9632 - age_out_accuracy: 0.7176 - val_loss: 0.9580 - val_sex_out_loss: 0.5303 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.7945 - val_age_out_accuracy: 0.7155\nEpoch 77/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5323 - sex_out_loss: 0.1065 - age_out_loss: 0.2822 - sex_out_accuracy: 0.9609 - age_out_accuracy: 0.7178\nEpoch 77: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5325 - sex_out_loss: 0.1065 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9609 - age_out_accuracy: 0.7176 - val_loss: 0.7521 - val_sex_out_loss: 0.3232 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8843 - val_age_out_accuracy: 0.7155\nEpoch 78/100\n275/278 [============================>.] - ETA: 0s - loss: 0.5243 - sex_out_loss: 0.0976 - age_out_loss: 0.2826 - sex_out_accuracy: 0.9652 - age_out_accuracy: 0.7174\nEpoch 78: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5241 - sex_out_loss: 0.0975 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9652 - age_out_accuracy: 0.7176 - val_loss: 0.7969 - val_sex_out_loss: 0.3680 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8785 - val_age_out_accuracy: 0.7155\nEpoch 79/100\n276/278 [============================>.] - ETA: 0s - loss: 0.5231 - sex_out_loss: 0.0974 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9640 - age_out_accuracy: 0.7176\nEpoch 79: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5238 - sex_out_loss: 0.0980 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9637 - age_out_accuracy: 0.7176 - val_loss: 0.7331 - val_sex_out_loss: 0.3045 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8794 - val_age_out_accuracy: 0.7155\nEpoch 80/100\n277/278 [============================>.] - ETA: 0s - loss: 0.5260 - sex_out_loss: 0.0998 - age_out_loss: 0.2823 - sex_out_accuracy: 0.9618 - age_out_accuracy: 0.7177\nEpoch 80: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 18ms/step - loss: 0.5267 - sex_out_loss: 0.1003 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9615 - age_out_accuracy: 0.7176 - val_loss: 0.7723 - val_sex_out_loss: 0.3439 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8563 - val_age_out_accuracy: 0.7155\nEpoch 81/100\n275/278 [============================>.] - ETA: 0s - loss: 0.5225 - sex_out_loss: 0.0961 - age_out_loss: 0.2830 - sex_out_accuracy: 0.9631 - age_out_accuracy: 0.7170\nEpoch 81: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5222 - sex_out_loss: 0.0963 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9632 - age_out_accuracy: 0.7176 - val_loss: 0.7916 - val_sex_out_loss: 0.3629 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8770 - val_age_out_accuracy: 0.7155\nEpoch 82/100\n278/278 [==============================] - ETA: 0s - loss: 0.5295 - sex_out_loss: 0.1023 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9622 - age_out_accuracy: 0.7176\nEpoch 82: val_loss did not improve from 0.66893\n278/278 [==============================] - 5s 17ms/step - loss: 0.5295 - sex_out_loss: 0.1023 - age_out_loss: 0.2824 - sex_out_accuracy: 0.9622 - age_out_accuracy: 0.7176 - val_loss: 0.8338 - val_sex_out_loss: 0.4045 - val_age_out_loss: 0.2845 - val_sex_out_accuracy: 0.8556 - val_age_out_accuracy: 0.7155\n","output_type":"stream"}]},{"cell_type":"code","source":"model.evaluate(x_test,y_test)","metadata":{"execution":{"iopub.status.busy":"2023-08-21T14:59:38.977742Z","iopub.execute_input":"2023-08-21T14:59:38.978166Z","iopub.status.idle":"2023-08-21T15:00:02.690658Z","shell.execute_reply.started":"2023-08-21T14:59:38.978126Z","shell.execute_reply":"2023-08-21T15:00:02.689703Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"186/186 [==============================] - 1s 5ms/step - loss: 0.6689 - sex_out_loss: 0.2898 - age_out_loss: 0.2845 - sex_out_accuracy: 0.8709 - age_out_accuracy: 0.7155\n","output_type":"stream"},{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"[0.6689291000366211,\n 0.28981542587280273,\n 0.2844609320163727,\n 0.8709296584129333,\n 0.7155390381813049]"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.title('Model Loss')\nplt.xlabel=('Epoch')\nplt.ylabel=('Loss')\nplt.legend(['Train','Validation'],loc='upper left')\nplt.subplots_adjust(top=1.0,bottom=0.0,right=0.95,left=0,hspace=0.25,wspace=0.35)","metadata":{"execution":{"iopub.status.busy":"2023-08-21T15:00:02.694034Z","iopub.execute_input":"2023-08-21T15:00:02.694351Z","iopub.status.idle":"2023-08-21T15:00:03.126597Z","shell.execute_reply.started":"2023-08-21T15:00:02.694323Z","shell.execute_reply":"2023-08-21T15:00:03.125703Z"},"trusted":true},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['sex_out_accuracy'])\nplt.plot(history.history['val_sex_out_accuracy'])\nplt.title('Model Accuracy')\nplt.xlabel=('Epoch')\nplt.ylabel=('Accuracy')\nplt.legend(['Train','Validation'],loc='upper left')\nplt.subplots_adjust(top=1.0,bottom=0.0,right=0.95,left=0,hspace=0.25,wspace=0.35)\n","metadata":{"execution":{"iopub.status.busy":"2023-08-21T15:13:30.554558Z","iopub.execute_input":"2023-08-21T15:13:30.554948Z","iopub.status.idle":"2023-08-21T15:13:30.931588Z","shell.execute_reply.started":"2023-08-21T15:13:30.554916Z","shell.execute_reply":"2023-08-21T15:13:30.930722Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"model_json = model.to_json()\nwith open(\"model_a.json\", \"w\") as json_file:\n    json_file.write(model_json)","metadata":{"execution":{"iopub.status.busy":"2023-08-21T15:13:40.110712Z","iopub.execute_input":"2023-08-21T15:13:40.111096Z","iopub.status.idle":"2023-08-21T15:13:40.125187Z","shell.execute_reply.started":"2023-08-21T15:13:40.111064Z","shell.execute_reply":"2023-08-21T15:13:40.124182Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}